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Zhan Zhang commented on SPARK-3720: ----------------------------------- By the way, I don't think the current approach can make into upstream. If you are interested, we can collaborate. > support ORC in spark sql > ------------------------ > > Key: SPARK-3720 > URL: https://issues.apache.org/jira/browse/SPARK-3720 > Project: Spark > Issue Type: New Feature > Components: SQL > Affects Versions: 1.1.0 > Reporter: wangfei > > The Optimized Row Columnar (ORC) file format provides a highly efficient way > to store data on hdfs.ORC file format has many advantages such as: > 1 a single file as the output of each task, which reduces the NameNode's load > 2 Hive type support including datetime, decimal, and the complex types > (struct, list, map, and union) > 3 light-weight indexes stored within the file > skip row groups that don't pass predicate filtering > seek to a given row > 4 block-mode compression based on data type > run-length encoding for integer columns > dictionary encoding for string columns > 5 concurrent reads of the same file using separate RecordReaders > 6 ability to split files without scanning for markers > 7 bound the amount of memory needed for reading or writing > 8 metadata stored using Protocol Buffers, which allows addition and removal > of fields > Now spark sql support Parquet, support ORC provide people more opts. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org